Qwen2.5-1M
The Qwen2.5-1M is an advanced open-source language model that processes context lengths of up to one million tokens. Featuring two variants, Qwen2.5-7B-Instruct-1M and Qwen2.5-14B-Instruct-1M, this model introduces an efficient inference framework leveraging sparse attention methods, achieving 3x to 7x faster processing speeds for extensive inputs.
Top Qwen2.5-1M Alternatives
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Qwen2-VL is an advanced vision-language model that excels in visual comprehension across various resolutions and ratios, achieving state-of-the-art results on benchmarks like MathVista and DocVQA.
DeepSeek-V3
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Qwen2.5-VL
Qwen2.5-VL is a cutting-edge vision-language model that excels in visual recognition and understanding various objects, texts, and layouts.
CodeQwen
CodeQwen, an advanced iteration of the Qwen series, specializes in code generation with remarkable proficiency across 92 programming languages.
Qwen2.5-Max
It showcases superior performance against competitors like DeepSeek V3 across various benchmarks, including Arena-Hard and...
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It seamlessly integrates fast and slow thinking to deliver intuitive responses and logical problem-solving...
Janus-Pro-7B
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Yi-Lightning
With a context length of 16K tokens and an economical pricing of $0.14 per million...
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Qwen2
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Top Qwen2.5-1M Features
- Open-source model availability
- Supports 1 million tokens
- Qwen2.5-7B-Instruct-1M model
- Qwen2.5-14B-Instruct-1M model
- Efficient inference framework
- Sparse attention integration
- 3x to 7x speed improvement
- Long-context processing capabilities
- Dual Chunk Attention method
- Enhanced kernel efficiency
- Dynamic chunked pipeline parallelism
- Minimal VRAM usage
- Instruction-tuned performance
- Passkey retrieval accuracy
- Comprehensive technical report
- Optimized deployment instructions
- Integration with Qwen-Agent
- Support for GPU architectures
- Advanced long-context tasks
- User-friendly interaction methods
